Rossmann is a well-known European drugstore chain that has been in operation since 1972. With over 3,000 stores in 7 countries, Rossmann is a go-to destination for a wide range of personal care, wellness, and household products. The company has earned a reputation for its commitment to sustainability, with a range of initiatives designed to reduce waste and promote sustainable products. In addition to its brick-and-mortar stores, Rossmann also operates an online store, making it easy for customers to shop for their favorite products from the comfort of their own homes. With its wide selection of high-quality products and commitment to sustainability, Rossmann is a leader in the European retail industry.
Rossmann is a leading drugstore chain headquartered in Germany with over 3,000 stores scattered throughout Europe. Its merchandise offerings encompass a wide range of products, including household goods, health and wellness products, and cosmetics.
In 2015, Rossmann hosted a Kaggle competition that focused on developing a model capable of accurately predicting store sales. The competition's dataset included historical sales data from 1,115 Rossmann stores, as well as pertinent information about competing stores, promotions, and other potential sales-influencing factors.
The primary objective of the competition was to devise an effective model that could predict the sales of each store for the next six weeks. The model's use would enable Rossmann to make more informed decisions about inventory management, promotions, and other business operations.
The Rossmann sales dataset poses a unique and intricate challenge for data scientists and machine learning experts. Constructing an accurate and dependable sales prediction model necessitates a blend of advanced data cleaning, feature engineering, and model selection methods.
Scrutinizing this dataset can yield valuable insights into the crucial factors that drive sales at Rossmann stores. It can also aid in identifying important trends and patterns that can boost the efficiency of the company's operations.
The Objectives of this project is to build a predictive model that can accurately forecast the sales of each store for the next six weeks. This will help Rossmann to optimize their business operations by enabling them to better plan for staffing, inventory, and promotions.
The data provided includes information about the stores, such as their size, location, and competition, as well as data on the sales, promotions, and holidays. It will be important to carefully analyze and preprocess this data before building the predictive model.
The project will require the use of machine learning techniques, such as regression, time-series analysis, and feature engineering. Additionally, it will be important to evaluate the performance of the model using appropriate metrics, such as mean squared error, root mean squared error, and R-squared.
Overall, the goal is to build a model that can accurately forecast sales across multiple locations, and to provide insights into which factors are most important in driving sales.
Rossmann is a German drugstore chain with over 3,000 stores across Europe. The company's sales objectives are likely to vary depending on the specific market and context in which the stores operate. However, some possible sales objectives for Rossmann Stores could include:
● 1.Increase overall sales revenue: One of the primary sales objectives for Rossmann is likely to be increasing overall sales revenue. This could involve increasing the number of customers who visit the stores, increasing the average transaction size, or both.
● 2.Expand customer base: Rossmann may also set an objective of expanding its customer base, which could involve targeting new demographics or geographic areas with advertising and marketing efforts.
● 3.Increase sales of specific products or categories: Rossmann may set specific sales objectives for certain product categories, such as beauty or personal care products, in order to drive revenue growth and increase market share in those areas.
● 4.Optimize pricing and promotions: Rossmann may also set sales objectives around pricing and promotions, such as increasing the number of sales and special offers, or optimizing pricing strategies to increase sales volume and profitability.
● 5.Increase online sales: As e-commerce continues to grow in importance, Rossmann may set objectives around increasing online sales through its website or mobile app, or through partnerships with online retailers or delivery services.
These are just a few possible sales objectives for Rossmann Stores, and the company is likely to have a more specific and detailed set of objectives based on its specific market and business goals.
Rossmann Sales store can provide valuable insights into sales performance, customer behavior, and other factors that impact the company's operations.
Sales performance by store location: A plot of daily sales by store location can provide insights into which stores are performing well and which ones may need additional support. This can help the company identify opportunities to optimize store operations and allocate resources more effectively.
Sales performance by product category: Analyzing sales by product category can help the company identify which categories are driving the most revenue and which ones may need additional attention. This can inform decisions about product assortment and promotions.
Customer behavior: Analyzing customer behavior data, such as purchasing patterns and loyalty program participation, can help the company understand its customer base and identify opportunities to improve customer satisfaction and loyalty.
External factors: Analyzing external data, such as weather patterns and competitor activities, can help the company understand the impact of these factors on sales performance and operations. This can inform decisions about pricing, promotions, and other marketing strategies.
Seasonal trends: Analyzing sales data over time can reveal seasonal trends and patterns, which can inform decisions about staffing levels, inventory management, and promotional activities.
By conducting EDA, Rossmann Sales store can gain a better understanding of its sales performance and operations, identify opportunities for optimization and improvement, and make data-driven decisions to drive revenue and enhance customer satisfaction.
The dataset for Rossmann Stores comprises sales data of 1,115 stores situated in Germany. It provides essential information like store ID, date, competition and promotional details, and other features to predict sales. The target is to create a model that can predict sales for each store for the upcoming six weeks.
With over a million data points, this dataset has both categorical and numerical variables. Since it's a time-series prediction problem, the data needs to be sorted based on date. There are also some missing values that require handling before creating models.
The Rossmann Stores dataset presents a unique challenge for data scientists and machine learning experts looking to develop accurate predictive models for retail sales forecasting.
It is worth noting that the dataset contains approximately 1 million data points, and due to the nature of the problem being a time-series prediction, the data must be sorted according to the date.
In this case, the variable we are aiming to predict is Sales.
Information about store.csv is as follows:
The dataset contains a total of 1,115 distinct stores, and several columns have missing values, which will be addressed shortly.
Rossmann is a retail chain with stores across Europe. The company operates three different types of stores: A, B, and C. Type A stores are the largest, with the widest selection of products and the highest sales volume. These stores are typically located in high-traffic areas such as shopping centers or on main streets. Type B stores are medium-sized and offer a slightly smaller selection of products than type A stores. These stores are often found in suburban or residential areas. Type C stores are the smallest and offer a limited selection of products. They are usually located in rural areas or small towns.
As of the latest available data, Rossmann had a total of 3,000 stores across Europe. Of these, type A stores, accounting for nearly 29% of the total. Type B stores were the most common, or approximately 67% of the total. Type C stores were the least common, with just 164 locations, or roughly 4% of the total.
Rossmann observes fluctuations in store sales across various months of the year, with the holiday season of November and December being the busiest due to the demand for holiday-related items. In contrast, January is a slower month, with customers focusing on recovering from holiday spending and New Year's resolutions. Furthermore, August, September, June, and July, also known as the back-to-school and summer months, experience increased sales due to customers' purchases of outdoor products. Rossmann needs to have a clear understanding of these patterns to forecast sales accurately and improve their operational efficiency.
Here we observe that Sales and Customers are both very less on Sundays as most of the stores are closed on Sunday.
Also, Sales on Monday is highest in whole week. This might be due to the fact that stores are closed on Sundays.
Based on the data analysis, it is evident that the occurrence of promotions is associated with a substantial increase in both Sales and Customers. This finding strongly suggests that promotions have a beneficial impact on a store's performance.
An intriguing observation is that there is a rise in sales during the Christmas and New Year holidays. the Rossmann Stores specializes in health and beauty products, it is plausible that consumers purchase beauty products as they prepare for social events and gatherings during the holiday season, contributing to the abrupt increase in sales.
An intriguing observation is that there is a rise in sales during the Christmas and New Year holidays. the Rossmann Stores specializes in health and beauty products, it is plausible that consumers purchase beauty products as they prepare for social events and gatherings during the holiday season, contributing to the abrupt increase in sales.
A noticeable trend in the data shows that many stores were closed during both State and School Holidays. However, what stands out is that there were more stores open during School Holidays than during State Holidays. Interestingly, these open stores during School Holidays also reported higher sales compared to normal days.
It is evident that the majority of stores faced competition from nearby competitors within a 4.5km-5km radius.
A large number of stores faced competition from newly opened competitors after the year 2000.
A significant portion of stores, approximately 45%, did not utilize Promo2 for their business. This observation indicates that a considerable number of stores may have chosen not to take advantage of this promotional opportunity for various reasons
feature engineering is a crucial step in preparing data for machine learning models. This involves transforming raw data into features that capture important aspects of sales performance, customer behavior, and external factors that could impact sales. By creating features that encompass various aspects such as sales performance, customer behavior, competitor activities, weather patterns, and store-level features, the resulting dataset can provide a comprehensive view of the store's operations. With this information, the store can make data-driven decisions to optimize its operations, increase revenue, and improve customer satisfaction.
● Limited Timeframe: The dataset only covers a period of two and a half years, from January 2013 to July 2015. This limited timeframe may not be enough to capture long-term trends and patterns, or to make reliable predictions for future sales performance.
● Limited Store Locations: The dataset only covers a subset of Rossmann's store locations, which may not be representative of the entire company. It's possible that the sales patterns and customer behavior in these stores are different from other stores, which could limit the generalizability of the insights derived from the data.
● Limited Customer Information: The dataset only contains limited customer information, such as demographics and purchasing behavior. This may not be enough to accurately capture the complexity of customer behavior and preferences, which could affect the reliability of recommendations and predictions.
● Limited External Factors: The dataset only includes a limited number of external factors, such as weather and local events. Other external factors, such as macroeconomic trends or competitor activity, may also impact sales performance, but are not included in the dataset.
● Lack of Causal Relationships: The dataset only captures correlations between different variables, and does not provide information about causal relationships. This means that it's difficult to determine why certain patterns and trends are occurring, and to make reliable predictions about the future based on these correlations alone.
● Overall, these limitations highlight the importance of being mindful of the context and scope of the data being used, and to use caution when making decisions based on the insights and predictions derived from the data. It's important to supplement data analysis with other sources of information and to consider a range of possible outcomes and scenarios.
The popularity of store type A and the strong correlation between sales and the number of customers highlight the importance of attracting and retaining customers. Promotions have proven to be an effective way to increase sales and customer traffic, and store openings during school holidays can help to capitalize on increased demand during those periods. Additionally, it is essential to keep an eye on competition, as the majority of stores face competition within a 5km radius. The impact of Promo2 on sales is not significant, and it might not be worth the investment for all stores to implement it. These insights, along with the use of feature engineering techniques to create more informative datasets, can help Rossmann make data-driven decisions to optimize its inventory, promotional strategies, and overall business operations to drive higher sales and revenue.